Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Комментарий: The Materials Project: подход «генома материалов» для ускорения инноваций в области материалов
2013-07-01
SCID: 54.1/6g54tb7a
Discuss with AI
Materials Genome InitiativeMaterials Projectdata-driven materials designhigh-throughput computingopen materials dataset
Figures from the paper
Abstract (AI)
Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design.
Key Findings
1
Materials Project develops open-source platforms for robust, sophisticated materials analyses.
2
Planned future efforts aim to enable rapid in silico prototyping of new materials and cost-effective, data-driven materials design.
3
The Materials Project is positioned as a core program of the Materials Genome Initiative to accelerate materials innovation for sustainable, clean energy.
4
The Materials Project uses high-throughput computing to compute and uncover properties of all known inorganic materials.
5
The project provides an open dataset accessible through multiple channels for interactive exploration and data mining.
Research Object
The Materials Project (open high-throughput computational materials database and platform)
Research Subject
Using high-throughput computing and open-source platforms to uncover and provide properties of known inorganic materials for interactive exploration, data mining, and in silico rapid-prototyping to accelerate materials discovery and design
Publication Details
Publication Date
2013-07-01
Journal
Publisher
ISSN
Open access PDF
Access Type
Author Information
Download PDF
Subscribe to digest
Cited by4
Improving machine-learning models in materials science through large datasets2024
Obtaining Robust Density Functional Tight-Binding Parameters for Solids across the Periodic Table2024
Recent advances and applications of machine learning in solid-state materials science2019
Li-ion battery materials: present and future2014